Reese Harper

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

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Text Summarization

Function Calling

Prompt Response Writing SFT

Freelancer Overview

I am a detail-oriented data quality specialist with over 8 years of experience in image-based question answering, annotation auditing, and AI training data validation. My background includes extensive work in computer vision projects, where I have reviewed and corrected labeled datasets, detected image and text misclassifications, and ensured the accuracy of extracted information from images such as signage, forms, and product labels. I am skilled in using annotation platforms like Labelbox, CVAT, and Scale Studio, and comfortable with QA tools including Jira, Asana, and Airtable. My strong command of English grammar and natural phrasing allows me to rewrite and clarify AI training data to meet rigorous quality standards. I consistently maintain high productivity and accuracy in remote, fast-paced environments, and have a proven track record of improving data quality for AI systems through structured audits and detailed reporting.

Labeling Experience

Senior QA Reviewer — Image & Text AI Training Data

Data Annotation TechImagePolygonPolyline

Evaluated and verified multimodal AI training data with images and text. Verified the correctness of classifications, bounding boxes, object descriptions, and question-and-answer questions based on images. Identified the precise text from images to check answers and find any OCR or description mistakes. Located problems like incorrect object classification, missing descriptions, and inconsistencies between images and answers. Rephrased ambiguous answers into clear, grammatically correct self-contained answers if required. Used structured evaluation criteria to ensure high quality, consistency, and adherence to policies with high productivity and accuracy in a remote QA setting.

Evaluated and verified multimodal AI training data with images and text. Verified the correctness of classifications, bounding boxes, object descriptions, and question-and-answer questions based on images. Identified the precise text from images to check answers and find any OCR or description mistakes. Located problems like incorrect object classification, missing descriptions, and inconsistencies between images and answers. Rephrased ambiguous answers into clear, grammatically correct self-contained answers if required. Used structured evaluation criteria to ensure high quality, consistency, and adherence to policies with high productivity and accuracy in a remote QA setting.

2016 - 2024

Education

Work History